Data Management Glossary
ETL
ETL stands for Extract, Transform, Load. ETL is a process used to move and prepare data from one system (often raw or messy) into another system (usually for analysis or reporting). It’s a foundational method in data integration and data warehousing. ETL is a term primarily used for structured data management, pioneered by companies like Informatica. A good resource to learn core principles of ETL and data warehousing is The Data Warehouse Institute (TDWI).
ETL Steps Defined
Extract
This is where data is pulled from various sources. Sources can include databases, files, APIs, cloud storage, logs, or unstructured formats like emails or PDFs. The goal is to gather raw data from disparate sources.
Transform
This is where the data is cleaned, formatted, or enriched. This might involve converting dates, removing duplicates, categorizing content, or deriving new fields (e.g., calculating age from birthdate). The goal is to make data usable and consistent.
Load
The transformed data is moved into a destination system, like a data warehouse, data lake, analytics platform, or application. The goal is to store data where it can be accessed, queried, and analyzed by data engineers, data scientist and other data analyst titles.
Why Use ETL?
ETL tools combine data from multiple sources. They are designed to makes data clean, accurate, and analysis-ready. ETL tools typically powers dashboards and business intelligence / analytics tools. A new, cloud-centric approach to data and application integration emerged in the past 10 years that Gartner calls integration platform as a service (iPaaS).
Common ETL Tools and Example:
- Traditional: Informatica, Talend, Microsoft SSIS
- Modern cloud-native: Fivetran, Airbyte, dbt (Transform only), AWS Glue, Azure Data Factory, SnapLogic
- For unstructured data: Apache Nifi, Apache Tika, custom Python pipelines (or alternative approaches like Komprise Intelligent Data Management)
Here is a common ETL example – a company wants to analyze sales across multiple stores:
- Extract sales data from each store’s system
- Transform it to a common format and currency
- Load it into a central data warehouse for reporting
ETL Challenges for Unstructured Data
Traditional ETL tools were designed for structured data (for example, data in relational databases), where schemas and rules are clearly defined. Unstructured data — such as PDFs, images, audio, and raw text — doesn’t conform to those rules and needs more flexible, intelligent processing.
The core problem with unstructured data is its lack of a common schema. You can’t take a video file, an audio file, or even three video files from three different applications and place them in a tabular format, because they have different contexts and semantics.
Other challenges for ETL tools and unstructured data include:
- Complexity of content AI needs semantics, not just structure; ETL often doesn’t understand meaning.
- AI models often need to work with streaming or near-real-time data whereas ETL is typically batch-based.
- Context AI services (e.g. LLMs or computer vision) require contextual, often multi-modal understanding. ETL doesn’t offer that.
That said, ETL-like processes are useful, especially at the preprocessing and data wrangling stage:
- Extract: Ingest unstructured content from various systems (e.g., files, images, emails).
- Transform: Clean and normalize formats (e.g., convert audio to text, extract text from PDFs).
- Extract metadata (e.g., timestamps, entities, classification).
- Enrich or annotate data (e.g., labeling for training ML models).
- Load: Push processed data to a vector store (e.g., Pinecone, FAISS) or cloud AI service (e.g., OpenAI, Azure AI).
- Store in a data lakehouse (e.g., Databricks, Snowflake) for further analysis or fine-tuning.
Modern ETL Alternative: AI Data Workflows
In many AI data workflows or pipelines, especially for unstructured data, the pattern shifts from ETL to ELT (Extract → Load → Transform) using modern data pipelines. Komprise provides unstructured AI data ingestion capabilities that are a better approach than using traditional ETL tools to address this use case. Learn more about AI Data Workflows. In this scenario, the Transform step is done after data has been copied/migrated/moved to the source where the data is stored, which increasingly is in an Object Storage system like Amazon S3 or Azure Blob. Workflows are iterative and prompt engineering RAG (retrieval-augmented generation) and model training / fine-tuning are part of the process.
Example pipeline for AI and unstructured data
- Ingest PDFs, audio, video → extract with OCR/speech-to-text tools
- Store in object storage or a document DB
- Run AI services (e.g., OpenAI for summaries, classification, embeddings)
- Index in a Global File Index or metadatabase for fast retrieval
- Use in downstream AI apps (chatbots, recommendation systems, etc.)
To make unstructured data usable and searchable for AI, organizations must enrich metadata beyond the basic attributes storage systems provide. This requires creating a global index across storage environments to gain visibility and then applying tags—either manually by knowledgeable users or automatically using AI tools. These enriched tags help identify sensitive data and classify information for specific use cases. Ensuring metadata stays with data during movement is critical, as transferring large unstructured datasets is costly and time-consuming. Therefore, precise metadata-driven classification enables efficient, secure AI data pipelines.
To conclude, traditional ETL tools are not well suited for feeding unstructured data to AI.
Ideal use cases for ETL tools include:
- Preprocessing raw files with custom transformations
- Feeding structured data sources to defined targets
Use cases not well suited for traditional ETL tools include:
- Real-time AI data pipelines, which require a data streaming / data lake architecture
- Semantic understanding & embedding, which require AI-native tooling
RAG, LLMs, semantic search.
ETL tools could play a role for AI data workflows, especially with unstructured data, they should be combined with modern AI services, flexible storage, and unstructured data management solutions.